Background

This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.

Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.

The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.

The code in this module builds on code available in _v003, with function and mapping files updated:

Broadly, the CDC data analyzed by this module includes:

Functions and Mapping Files

The tidyverse package is loaded and functions are sourced:

# The tidyverse functions are routinely used without package::function format
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.1     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(geofacet)
## Warning: package 'geofacet' was built under R version 4.1.2
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")

A series of mapping files are also available to allow for parameterized processing. Mappings include:

These default parameters are maintained in a separate .R file and can be sourced:

source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")

Example Code Processing

The function is run to download and process the latest CDC case, hospitalization, and death data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220220.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220220.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220220.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220206")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220206")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220206")$dfRaw$vax
                    )

cdc_daily_220220 <- readRunCDCDaily(thruLabel="Feb 18, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-01-30 new_deaths      796      539      257 0.38501873
## 2  2022-01-29 new_deaths     1394     1098      296 0.23756019
## 3  2022-01-23 new_deaths      868      709      159 0.20164870
## 4  2022-01-22 new_deaths     1176     1028      148 0.13430127
## 5  2022-01-16 new_deaths      807      747       60 0.07722008
## 6  2022-01-25 new_deaths     3445     3220      225 0.06751688
## 7  2022-01-24 new_deaths     2679     2505      174 0.06712963
## 8  2022-01-27 new_deaths     2757     2592      165 0.06169377
## 9  2022-01-17 new_deaths     1429     1350       79 0.05685498
## 10 2022-01-26 new_deaths     3023     2858      165 0.05611291
## 11 2022-01-29  new_cases   195076   173891    21185 0.11483412
## 12 2022-01-30  new_cases   138089   124992    13097 0.09956629
## 13 2022-01-31  new_cases   620416   661083    40667 0.06346786
## 14 2022-02-04  new_cases   272825   289747    16922 0.06015941

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name   newValue   refValue absDelta    pctDelta
## 1     KY tot_deaths    4003629    3992287    11342 0.002836948
## 2     AL tot_deaths    5972978    5963555     9423 0.001578850
## 3     NC tot_deaths    6808527    6798521    10006 0.001470708
## 4     FL  tot_cases 1290393798 1286243847  4149951 0.003221214
## 5     MD  tot_cases  232491171  231793719   697452 0.003004414
## 6     KY  tot_cases  252489934  252077588   412346 0.001634453
## 7     FL new_deaths      68042      66007     2035 0.030362032
## 8     KY new_deaths      13402      13063      339 0.025618742
## 9     AL new_deaths      17741      17371      370 0.021075416
## 10    NC new_deaths      21278      21097      181 0.008542773
## 11    RI new_deaths       3358       3354        4 0.001191895
## 12    MD  new_cases     984492     961805    22687 0.023312989
## 13    KY  new_cases    1208554    1193647    14907 0.012411118
## 14    TN  new_cases    1912511    1926401    13890 0.007236425
## 15    FL  new_cases    5648704    5629602    19102 0.003387388
## 16    NC  new_cases    2478266    2470242     8024 0.003242998
## 17    SC  new_cases    1408611    1405271     3340 0.002373945
## 18    RI  new_cases     348326     347901      425 0.001220866
## 19    PW  new_cases       2498       2495        3 0.001201682
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 45,540
## Columns: 15
## $ date           <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-0~
## $ state          <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "~
## $ tot_cases      <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 28~
## $ conf_cases     <dbl> 135705, NA, 3685032, NA, NA, 1130917, NA, 176228, NA, 7~
## $ prob_cases     <dbl> 27860, NA, 0, NA, NA, 0, NA, 103954, NA, 108997, 0, NA,~
## $ new_cases      <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 5~
## $ pnew_case      <dbl> 220, 0, 0, 0, 0, 0, 0, 559, 0, 443, 0, 0, 0, 0, NA, 0, ~
## $ tot_deaths     <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408~
## $ conf_death     <dbl> NA, 3616, 62011, 9604, NA, 19306, NA, 4739, NA, 10976, ~
## $ prob_death     <dbl> NA, 149, 0, 218, NA, 2030, NA, 1991, NA, 1432, NA, 416,~
## $ new_deaths     <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, ~
## $ pnew_death     <dbl> 0, 0, 0, 1, 0, 16, 0, 7, 0, 2, 0, 0, 0, 0, NA, 0, 0, 4,~
## $ created_at     <chr> "12/02/2021 02:35:20 PM", "08/19/2020 12:00:00 AM", "06~
## $ consent_cases  <chr> "Agree", "N/A", "Agree", "N/A", "Not agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Not agree", "A~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-02-05        inp   108309   114478     6169 0.05538025
## 2 2022-02-05   hosp_ped     3323     3585      262 0.07585408
## 3 2021-11-24   hosp_ped     1387     1306       81 0.06015596
## 4 2022-02-05 hosp_adult   104794   110893     6099 0.05655417

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state     name newValue refValue absDelta    pctDelta
## 1     NH hosp_ped      725      811       86 0.111979167
## 2     ME hosp_ped     1373     1431       58 0.041369472
## 3     WV hosp_ped     4435     4554      119 0.026476805
## 4     VT hosp_ped      348      357        9 0.025531915
## 5     AR hosp_ped    10602    10393      209 0.019909502
## 6     KS hosp_ped     3856     3929       73 0.018754014
## 7     SC hosp_ped     7275     7393      118 0.016089446
## 8     VI hosp_ped       81       80        1 0.012422360
## 9     MA hosp_ped     9296     9412      116 0.012401112
## 10    ID hosp_ped     3155     3120       35 0.011155378
## 11    KY hosp_ped    15228    15375      147 0.009606901
## 12    NJ hosp_ped    15981    15838      143 0.008988340
## 13    IN hosp_ped    14697    14787       90 0.006105006
## 14    UT hosp_ped     7026     6998       28 0.003993155
## 15    NV hosp_ped     3856     3871       15 0.003882490
## 16    ND hosp_ped     2898     2909       11 0.003788531
## 17    TN hosp_ped    17497    17563       66 0.003764974
## 18    AL hosp_ped    17263    17319       56 0.003238679
## 19    NC hosp_ped    23574    23649       75 0.003176418
## 20    OR hosp_ped     7333     7356       23 0.003131595
## 21    MO hosp_ped    31461    31363       98 0.003119827
## 22    MS hosp_ped     8953     8926       27 0.003020303
## 23    PA hosp_ped    43632    43509      123 0.002823011
## 24    GA hosp_ped    42185    42079      106 0.002515902
## 25    IA hosp_ped     6153     6168       15 0.002434867
## 26    HI hosp_ped     1909     1913        4 0.002093145
## 27    AZ hosp_ped    22800    22847       47 0.002059281
## 28    NE hosp_ped     6181     6170       11 0.001781232
## 29    WA hosp_ped    10469    10484       15 0.001431776
## 30    CO hosp_ped    17474    17499       25 0.001429674
## 31    WI hosp_ped     8578     8568       10 0.001166453
## 32    IL hosp_ped    35034    35073       39 0.001112585
## 33    OK hosp_ped    20546    20524       22 0.001071342
## 34    RI hosp_ped     2843     2846        3 0.001054667
## 35    PR hosp_ped    16962    16979       17 0.001001738
## 36    AK hosp_ped     1996     1998        2 0.001001502
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 38,675
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 27,992
## Columns: 82
## $ date                                   <date> 2022-02-19, 2022-02-19, 2022-0~
## $ MMWR_week                              <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
## $ state                                  <chr> "NC", "TN", "MN", "MI", "SD", "~
## $ Distributed                            <dbl> 20744900, 12186030, 11914970, 1~
## $ Distributed_Janssen                    <dbl> 916100, 503900, 500200, 926300,~
## $ Distributed_Moderna                    <dbl> 7813760, 4644240, 4216760, 7835~
## $ Distributed_Pfizer                     <dbl> 12015040, 7037890, 7198010, 111~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 197795, 178441, 211272, 199329,~
## $ Distributed_Per_100k_12Plus            <dbl> 230870, 208823, 249367, 231608,~
## $ Distributed_Per_100k_18Plus            <dbl> 253377, 229098, 274762, 253818,~
## $ Distributed_Per_100k_65Plus            <dbl> 1184680, 1065780, 1294570, 1127~
## $ vxa                                    <dbl> 16040239, 9551129, 9853584, 150~
## $ Administered_12Plus                    <dbl> 15576577, 9369683, 9460637, 146~
## $ Administered_18Plus                    <dbl> 14630091, 8914389, 8826086, 138~
## $ Administered_65Plus                    <dbl> 4239236, 2778123, 2487485, 4293~
## $ Administered_Janssen                   <dbl> 508845, 259901, 353693, 459665,~
## $ Administered_Moderna                   <dbl> 5969173, 3654211, 3581985, 5900~
## $ Administered_Pfizer                    <dbl> 9561290, 5583600, 5913885, 8724~
## $ Administered_Unk_Manuf                 <dbl> 931, 53417, 4021, 2106, 133, 32~
## $ Admin_Per_100k                         <dbl> 152938, 139858, 174720, 151062,~
## $ Admin_Per_100k_12Plus                  <dbl> 173352, 160562, 198000, 170666,~
## $ Admin_Per_100k_18Plus                  <dbl> 178691, 167591, 203531, 176626,~
## $ Admin_Per_100k_65Plus                  <dbl> 242091, 242972, 270267, 243193,~
## $ Recip_Administered                     <dbl> 15939232, 9383280, 9868373, 153~
## $ Administered_Dose1_Recip               <dbl> 8596653, 4180275, 4183752, 6576~
## $ Administered_Dose1_Pop_Pct             <dbl> 82.0, 61.2, 74.2, 65.9, 74.7, 0~
## $ Administered_Dose1_Recip_12Plus        <dbl> 8331132, 4079234, 3968078, 6351~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 92.7, 69.9, 83.0, 73.9, 86.3, 0~
## $ Administered_Dose1_Recip_18Plus        <dbl> 7823417, 3850407, 3680383, 5966~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 72.4, 84.9, 76.1, 88.9, 0~
## $ Administered_Dose1_Recip_65Plus        <dbl> 2154949, 1047531, 937204, 16884~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.6, 95.0, 95.0, 95.0, 0~
## $ vxc                                    <dbl> 6201249, 3646584, 3830382, 5889~
## $ vxcpoppct                              <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 0~
## $ Series_Complete_12Plus                 <dbl> 6011177, 3568185, 3651613, 5701~
## $ Series_Complete_12PlusPop_Pct          <dbl> 66.9, 61.1, 76.4, 66.3, 69.0, 0~
## $ vxcgte18                               <dbl> 5622542, 3375377, 3382210, 5355~
## $ vxcgte18pct                            <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 0~
## $ vxcgte65                               <dbl> 1498685, 956337, 876804, 153840~
## $ vxcgte65pct                            <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 0~
## $ Series_Complete_Janssen                <dbl> 477185, 232189, 326034, 416641,~
## $ Series_Complete_Moderna                <dbl> 2152155, 1299423, 1287594, 2139~
## $ Series_Complete_Pfizer                 <dbl> 3571765, 2103546, 2215307, 3333~
## $ Series_Complete_Unk_Manuf              <dbl> 144, 11426, 1447, 1082, 34, 0, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 477158, 232135, 326016, 416612,~
## $ Series_Complete_Moderna_12Plus         <dbl> 2152040, 1299371, 1287540, 2138~
## $ Series_Complete_Pfizer_12Plus          <dbl> 3381836, 2025317, 2036626, 3144~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 143, 11362, 1431, 1073, 34, 0, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 475728, 231891, 325496, 416312,~
## $ Series_Complete_Moderna_18Plus         <dbl> 2149019, 1298802, 1285260, 2138~
## $ Series_Complete_Pfizer_18Plus          <dbl> 2997656, 1833427, 1770066, 2799~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 139, 11257, 1388, 986, 34, 0, 5~
## $ Series_Complete_Janssen_65Plus         <dbl> 54321, 35691, 50477, 70861, 498~
## $ Series_Complete_Moderna_65Plus         <dbl> 720300, 474871, 369087, 768663,~
## $ Series_Complete_Pfizer_65Plus          <dbl> 723999, 439839, 456889, 698286,~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 65, 5936, 351, 592, 21, 0, 2511~
## $ Additional_Doses                       <dbl> 1544360, 1529958, 2125396, 2985~
## $ Additional_Doses_Vax_Pct               <dbl> 24.9, 42.0, 55.5, 50.7, 39.8, 2~
## $ Additional_Doses_12Plus                <dbl> 1544252, 1529687, 2125156, 2985~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 25.7, 42.9, 58.2, 52.4, 41.2, 2~
## $ Additional_Doses_18Plus                <dbl> 1500845, 1502838, 2049913, 2906~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 26.7, 44.5, 60.6, 54.3, 43.1, 2~
## $ Additional_Doses_50Plus                <dbl> 1017165, 1059552, 1281534, 1976~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 33.8, 56.3, 72.6, 64.9, 54.8, 4~
## $ Additional_Doses_65Plus                <dbl> 578981, 632802, 708477, 1135879~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 38.6, 66.2, 80.8, 73.8, 62.9, 5~
## $ Additional_Doses_Moderna               <dbl> 680325, 649042, 857058, 1316220~
## $ Additional_Doses_Pfizer                <dbl> 836934, 856740, 1239887, 162309~
## $ Additional_Doses_Janssen               <dbl> 27082, 20983, 28141, 46218, 254~
## $ Additional_Doses_Unk_Manuf             <dbl> 19, 3193, 310, 106, 9, 22, 648,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 8594663, 4179589, 4181728, 6576~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 87.0, 65.1, 79.1, 69.8, 80.3, 0~
## $ Series_Complete_5Plus                  <dbl> 6200658, 3646444, 3829701, 5889~
## $ Series_Complete_5PlusPop_Pct           <dbl> 62.8, 56.8, 72.4, 62.5, 64.1, 0~
## $ Administered_5Plus                     <dbl> 16037693, 9550281, 9850893, 150~
## $ Admin_Per_100k_5Plus                   <dbl> 162353, 148745, 186287, 160139,~
## $ Distributed_Per_100k_5Plus             <dbl> 210004, 189797, 225320, 211315,~
## $ Series_Complete_Moderna_5Plus          <dbl> 2152112, 1299406, 1287586, 2138~
## $ Series_Complete_Pfizer_5Plus           <dbl> 3571235, 2103464, 2214653, 3333~
## $ Series_Complete_Janssen_5Plus          <dbl> 477168, 232150, 326019, 416627,~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 143, 11424, 1443, 1081, 34, 0, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  1.84e+10    3.17e+8   7.79e+7 910251       44781    
## 2 after   1.83e+10    3.15e+8   7.73e+7 905741       38709    
## 3 pctchg  4.83e- 3    4.32e-3   6.93e-3      0.00495     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 38,709
## Columns: 6
## $ date       <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-05-13~
## $ state      <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "NC",~
## $ tot_cases  <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 280182~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408, 55~
## $ new_cases  <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 537, ~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, 11, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult    hosp_ped          n
##   <chr>    <dbl>      <dbl>       <dbl>      <dbl>
## 1 before 4.51e+7    3.88e+7 945228      38675     
## 2 after  4.49e+7    3.86e+7 927959      37083     
## 3 pctchg 4.84e-3    4.63e-3      0.0183     0.0412
## 
## 
## Processed for cdcHosp:
## Rows: 37,083
## Columns: 5
## $ date       <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state      <chr> "HI", "NE", "IA", "NH", "HI", "DC", "KS", "NM", "ME", "NE",~
## $ inp        <dbl> 111, 376, 497, 45, 110, 166, 474, 189, 23, 316, 546, 3246, ~
## $ hosp_adult <dbl> 111, 367, 487, 44, 108, 149, 454, 186, 23, 315, 534, 3104, ~
## $ hosp_ped   <dbl> 0, 9, 10, 1, 2, 17, 5, 3, 0, 6, 12, 55, 8, 0, 1, 8, 2, 8, 6~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 2.66e+11 1.13e+11 1003870.    3.03e+10 1559557.    1.06e+11 1202494.   
## 2 after  1.28e+11 5.46e+10  843159.    1.46e+10 1396120.    5.14e+10 1020516.   
## 3 pctchg 5.20e- 1 5.16e- 1       0.160 5.16e- 1       0.105 5.17e- 1       0.151
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 22,083
## Columns: 9
## $ date        <date> 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-1~
## $ state       <chr> "NC", "TN", "MN", "MI", "SD", "OH", "MT", "WV", "VA", "IA"~
## $ vxa         <dbl> 16040239, 9551129, 9853584, 15086338, 1349798, 17152418, 1~
## $ vxc         <dbl> 6201249, 3646584, 3830382, 5889772, 527824, 6712161, 59625~
## $ vxcpoppct   <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 57.4, 55.8, 56.6, 71.7, 60.9~
## $ vxcgte65    <dbl> 1498685, 956337, 876804, 1538402, 140420, 1779459, 175563,~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 87.0, 85.0, 83.5, 91.2, 92.0~
## $ vxcgte18    <dbl> 5622542, 3375377, 3382210, 5355218, 476217, 6118597, 54652~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 67.2, 65.0, 65.8, 81.0, 71.7~
## 
## Integrated per capita data file:
## Rows: 38,973
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220220, ovrWriteError=FALSE)

The latest hospital data are downloaded:

# Run for latest data, save as RDS
indivHosp_20220221 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220221.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 399,863
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         7503
## 2 Critical Access Hospitals 106952
## 3 Long Term                  27474
## 4 Short Term                257934
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       25
## 2 GU      160
## 3 MP       80
## 4 PR     4400
## 5 VI      160
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        11667   387469             727 399863
## 2 all_adult_hospital_inpatient_bed_occupi~  3328   364400           32135 399863
## 3 icu_beds_used_7_day_avg                   1649   350757           47457 399863
## 4 inpatient_beds_7_day_avg                  1730   396567            1566 399863
## 5 staffed_icu_adult_patients_confirmed_an~  4251   279744          115868 399863
## 6 total_adult_patients_hospitalized_confi~  2372   278715          118776 399863
## 7 total_beds_7_day_avg                      6632   392858             373 399863
## 8 total_icu_beds_7_day_avg                  2064   377884           19915 399863
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220221, ovrWriteError=FALSE)

The post-processing capabilities are included:

# Create pivoted burden data
burdenPivotList_220220 <- postProcessCDCDaily(cdc_daily_220220, 
                                              dataThruLabel="Jan 2022", 
                                              keyDatesBurden=c("2022-01-31", "2021-07-31", 
                                                               "2021-01-31", "2020-07-31"
                                                               ),
                                              keyDatesVaccine=c("2021-12-31", "2021-09-30", 
                                                                "2021-06-30", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

The hospital summaries are also added:

# Can be run only as-needed
dfStateAgeBucket2019 <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
    filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
    bucketPopStateAge(popVar="pop2019")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   SUMLEV = col_character(),
##   REGION = col_double(),
##   DIVISION = col_double(),
##   STATE = col_double(),
##   NAME = col_character(),
##   SEX = col_double(),
##   AGE = col_double(),
##   ESTBASE2010_CIV = col_double(),
##   POPEST2010_CIV = col_double(),
##   POPEST2011_CIV = col_double(),
##   POPEST2012_CIV = col_double(),
##   POPEST2013_CIV = col_double(),
##   POPEST2014_CIV = col_double(),
##   POPEST2015_CIV = col_double(),
##   POPEST2016_CIV = col_double(),
##   POPEST2017_CIV = col_double(),
##   POPEST2018_CIV = col_double(),
##   POPEST2019_CIV = col_double()
## )
## 
## *** File has been checked for uniqueness by: NAME SEX AGE 
## 
## [1] TRUE
## [1] TRUE
## [1] TRUE

## 
## PASSED CHECK: United States total is the sum of states and DC 
## 
## 
## PASSED CHECK: Age 999 total is the sum of the ages 
## 
## 
## PASSED CHECK: Sex 0 total is the sum of the sexes

# Create hospitalized per capita data
hospPerCap_220220 <- hospAgePerCapita(dfStateAgeBucket2019, 
                                      lst=burdenPivotList_220220, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

The one-page CFR plot capability is included:

# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70), 
                  "LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80), 
                  "CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100), 
                  "IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
                  )
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_220220$dfPivot, 
                                                     keyState=x$keyState, 
                                                     minDate=x$minDate, 
                                                     multDeath=x$multDeath
                                                     )
            )

The peaks and valleys plots are included:

# Burden data
cdc_daily_220220$dfPerCapita %>%
    mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
    makePeakValley(numVar=c("new_deaths", "new_cases", "inp"), 
                   windowWidth = 71, 
                   rollMean=7, 
                   facetVar=c("regn"), 
                   fnNumVar=list("new_deaths"=function(x) x, 
                                 "new_cases"=function(x) x/1000,
                                 "inp"=function(x) x/1000
                                 ), 
                   fnPeak=list("new_deaths"=function(x) x+100, 
                               "new_cases"=function(x) x+10, 
                               "inp"=function(x) x+10
                               ),
                   fnValley=list("new_deaths"=function(x) x-100, 
                                 "new_cases"=function(x) x-5, 
                                 "inp"=function(x) x-5
                                 ),
                   useTitle=c("new_deaths"="US coronavirus deaths", 
                              "new_cases"="US coronavirus cases", 
                              "inp"="US coronavirus total hospitalized"
                              ), 
                   yLab=c("new_deaths"="Rolling 7-day mean deaths", 
                          "new_cases"="Rolling 7-day mean cases (000)", 
                          "inp"="Rolling 7-day mean in hospital (000)"
                          )
                   )
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## # A tibble: 3,107 x 11
##    date       regn  new_deaths new_cases   inp new_deaths_isPe~ new_cases_isPeak
##    <date>     <chr>      <dbl>     <dbl> <dbl> <lgl>            <lgl>           
##  1 2020-01-01 Nort~         NA        NA    NA FALSE            FALSE           
##  2 2020-01-01 South         NA        NA    NA FALSE            FALSE           
##  3 2020-01-01 West          NA        NA    NA FALSE            FALSE           
##  4 2020-01-02 Nort~         NA        NA    NA FALSE            FALSE           
##  5 2020-01-02 South         NA        NA    NA FALSE            FALSE           
##  6 2020-01-02 West          NA        NA    NA FALSE            FALSE           
##  7 2020-01-03 Nort~         NA        NA    NA FALSE            FALSE           
##  8 2020-01-03 South         NA        NA    NA FALSE            FALSE           
##  9 2020-01-03 West          NA        NA    NA FALSE            FALSE           
## 10 2020-01-04 Nort~          0         0     0 FALSE            FALSE           
## # ... with 3,097 more rows, and 4 more variables: inp_isPeak <lgl>,
## #   new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl>
# Vaccinations data for states with 8+ million population
cdc_daily_220220$dfPerCapita %>%
    inner_join(getStateData(), by=c("state")) %>%
    filter(pop >= 8000000) %>%
    select(date, state, vxa, vxc) %>%
    arrange(date, state) %>%
    group_by(state) %>%
    mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
    ungroup() %>%
    mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
    filter(date >= "2020-12-01") %>%
    makePeakValley(numVar=c("vxc", "vxa"), 
                   windowWidth = 29, 
                   rollMean=21, 
                   facetVar=c("state"), 
                   fnNumVar=list("vxa"=function(x) x/1000, 
                                 "vxc"=function(x) x/1000
                                 ), 
                   fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400, 
                               "vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
                               ),
                   fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400, 
                                 "vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
                                 ),
                   fnGroupFacet=TRUE,
                   useTitle=c("vxa"="Vaccines adminsitered (US)", 
                              "vxc"="Became fully vaccinated (US)"
                              ), 
                   yLab=c("vxa"="Rolling 21-day mean administered (000)",
                          "vxc"="Rolling 21-day mean completed (000)"
                          )
                   )
## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 5,364 x 8
##    date       state   vxc   vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # ... with 5,354 more rows

The hospital utlization plots are included:

indivHosp_20220221 %>% 
    filter(state %in% c(state.abb, "DC"), 
           collection_week==max(collection_week)
    ) %>% 
    pull(hospital_pk) %>%
    plotHospitalUtilization(df=indivHosp_20220221, keyHosp=., plotTitle="US Hospitals Summed")

Imputed hospital utilization data are also created, using functional form:

# Impute values for hospital capacity
imputeNACapacity <- function(df, 
                             keyStates=c(state.abb, "DC"), 
                             varMapper=hhsMapper, 
                             varsToImpute=c("total_beds", "adult_beds"), 
                             varUsedToImpute=c("inpatient_beds")
                             ) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # keyState: states to include for filtering
    # varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
    # varsToImpute: variables to be imputed
    # varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
    
    df %>%
        filter(state %in% all_of(keyStates)) %>%
        colSelector(c("state", "collection_week", "hospital_pk", names(varMapper))) %>%
        colRenamer(varMapper) %>%
        mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), NA, ifelse(x==-999999, NA, x)))) %>%
        arrange(hospital_pk, collection_week) %>%
        group_by(hospital_pk) %>%
        mutate(across(all_of(varsToImpute), 
                      .fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=-999999)
                      )
               ) %>%
        group_by(state, collection_week) %>%
        summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}

modStateHosp_20220221 <- imputeNACapacity(indivHosp_20220221)

The function is split so that it is more generic:

# Select and filter as needed
skinnyHHS <- function(df, 
                      keyStates=c(state.abb, "DC"), 
                      idCols=c("state", "collection_week", "hospital_pk"),
                      varMapper=hhsMapper
                      ) {

    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # keyState: states to include for filtering
    # varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
    
    df %>%
        filter(state %in% all_of(keyStates)) %>%
        colSelector(c(all_of(idCols), names(varMapper))) %>%
        colRenamer(varMapper)
        
}

# Impute values for hospital capacity
imputeNACapacity <- function(df, 
                             extraNA=c(-999999),
                             convertAllNA=TRUE,
                             idVars=c("hospital_pk"), 
                             sortVars=c("collection_week"),
                             varsToImpute=c("total_beds", "adult_beds"), 
                             varUsedToImpute=c("inpatient_beds")
                             ) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # extraNA: values that should be treated as NA
    # convertAllNA: boolean, should all extraNA values be converted in all numeric columns?
    #               if FALSE, extraNA values will not be converted, though imputing will treat as NA
    # varsToImpute: variables to be imputed
    # varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
    
    # Convert NA if requested
    if(isTRUE(convertAllNA)) {
        df <- df %>%
            mutate(across(where(is.numeric), 
                          .fns=function(x) ifelse(is.na(x), NA, ifelse(x %in% all_of(extraNA), NA, x))
                          )
                   )
    }
    
    # Impute values and return data
    df %>%
        arrange(across(all_of(c(idVars, sortVars)))) %>%
        group_by(across(all_of(idVars))) %>%
        mutate(across(all_of(varsToImpute), 
                      .fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=extraNA)
                      )
               ) %>%
        ungroup()
    
}

sumImputedHHS <- function(df, 
                          groupVars=c("state", "collection_week")) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # groupVars: variables for summing the data to

    df %>%
        group_by(across(all_of(groupVars))) %>%
        summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
    
}

identical(skinnyHHS(indivHosp_20220221) %>%
              imputeNACapacity() %>%
              sumImputedHHS(), 
          modStateHosp_20220221
          )
## [1] TRUE

Updated maps with imputed capacity are created:

modStateHosp_20220221 <- skinnyHHS(indivHosp_20220221) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220221, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to January 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
createGeoMap(modStateHosp_20220221 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to January 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

Example Data Update

The function is run to download and process the latest data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220304.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220304.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220304.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220220")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220220")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220220")$dfRaw$vax
                    )

cdc_daily_220304 <- readRunCDCDaily(thruLabel="Mar 2, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2020-03-03  tot_cases      175      188       13 0.07162534
## 2  2022-02-13 new_deaths      615      446      169 0.31856739
## 3  2022-02-06 new_deaths      609      472      137 0.25346901
## 4  2022-02-12 new_deaths      891      695      196 0.24716267
## 5  2022-02-05 new_deaths     1158      989      169 0.15742897
## 6  2022-01-30 new_deaths      869      796       73 0.08768769
## 7  2022-02-07 new_deaths     3177     3000      177 0.05730937
## 8  2022-02-08 new_deaths     3704     3504      200 0.05549390
## 9  2022-02-03 new_deaths     2653     2515      138 0.05340557
## 10 2022-01-29 new_deaths     1469     1394       75 0.05239260
## 11 2022-02-11 new_deaths     2775     2638      137 0.05061888
## 12 2020-03-03  new_cases       51       64       13 0.22608696
## 13 2022-02-12  new_cases    66377    55089    11288 0.18586271
## 14 2022-02-13  new_cases    47803    40950     6853 0.15442858
## 15 2022-01-30  new_cases   155259   138089    17170 0.11706233
## 16 2022-02-05  new_cases   102256    91295    10961 0.11326214
## 17 2022-02-14  new_cases   178028   199342    21314 0.11296075
## 18 2022-02-11  new_cases   155537   172496    16959 0.10339813
## 19 2022-01-29  new_cases   215839   195076    20763 0.10105740
## 20 2020-03-07  new_cases      146      160       14 0.09150327
## 21 2021-10-31  new_cases    22766    20850     1916 0.08785767
## 22 2021-11-06  new_cases    32140    29452     2688 0.08728406
## 23 2021-10-24  new_cases    25952    23899     2053 0.08236545
## 24 2021-11-07  new_cases    28368    26379     1989 0.07266152
## 25 2020-03-06  new_cases      130      121        9 0.07171315
## 26 2021-10-23  new_cases    33628    31349     2279 0.07014790
## 27 2022-01-22  new_cases   320403   299989    20414 0.06581000
## 28 2022-02-06  new_cases    96184    90271     5913 0.06342549
## 29 2022-01-18  new_cases   861976   917498    55522 0.06240271
## 30 2020-03-09  new_cases      390      415       25 0.06211180
## 31 2022-01-31  new_cases   583405   620416    37011 0.06148921
## 32 2022-01-23  new_cases   310096   291779    18317 0.06086646
## 33 2021-11-14  new_cases    30649    28992     1657 0.05556580
## 34 2021-11-20  new_cases    42759    40531     2228 0.05349982
## 35 2021-12-25  new_cases   126095   119545     6550 0.05333008
## 36 2021-10-30  new_cases    31410    29822     1588 0.05186830
## 37 2021-05-24  new_cases    15400    16206      806 0.05100297

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     RI tot_deaths   1395734   1419362    23628 0.016786639
## 2     FL tot_deaths  22083833  22194156   110323 0.004983198
## 3     KY tot_deaths   4208985   4192009    16976 0.004041427
## 4     NC tot_deaths   7123584   7111646    11938 0.001677247
## 5     AL tot_deaths   6231481   6222060     9421 0.001512983
## 6     RI  tot_cases  75530417  79533518  4003101 0.051631619
## 7     ME  tot_cases  38751314  36951188  1800126 0.047557900
## 8     WA  tot_cases 263650336 264272454   622118 0.002356852
## 9     KY  tot_cases 270113183 269767213   345970 0.001281654
## 10    AL new_deaths     18381     17877      504 0.027800761
## 11    FL new_deaths     70406     68581     1825 0.026261449
## 12    WA new_deaths     11615     11316      299 0.026078235
## 13    KY new_deaths     13885     13565      320 0.023315118
## 14    NC new_deaths     22277     22148      129 0.005807541
## 15    ME  new_cases    225203    212435    12768 0.058349595
## 16    RI  new_cases    336543    354045    17502 0.050687240
## 17    WA  new_cases   1396813   1410596    13783 0.009819018
## 18    KY  new_cases   1265367   1258310     7057 0.005592633
## 19    SD  new_cases    234285    234961      676 0.002881218
## 20    NC  new_cases   2563976   2559793     4183 0.001632782
## 21    SC  new_cases   1451483   1449247     2236 0.001541681
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 46,260
## Columns: 15
## $ date           <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-0~
## $ state          <chr> "KS", "UT", "CO", "AR", "AR", "CO", "PW", "UT", "MA", "~
## $ tot_cases      <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 0, 636992, 7~
## $ conf_cases     <dbl> 241035, 359641, 411869, NA, NA, 1117524, NA, 636992, 65~
## $ prob_cases     <dbl> 56194, 0, 26876, NA, NA, 105369, NA, 0, 45550, 321, NA,~
## $ new_cases      <dbl> 0, 1060, 677, 0, 547, 6962, 0, 0, 451, 619, 69, 24010, ~
## $ pnew_case      <dbl> 0, 0, 60, NA, 0, 1247, 0, 0, 46, 1, 10, 4196, 264, 3202~
## $ tot_deaths     <dbl> 4851, 1785, 5952, 0, 674, 10953, 0, 3787, 17818, 805, 8~
## $ conf_death     <dbl> NA, 1729, 5218, NA, NA, 9666, NA, 3635, 17458, 624, NA,~
## $ prob_death     <dbl> NA, 56, 734, NA, NA, 1287, NA, 152, 360, 181, NA, NA, 1~
## $ new_deaths     <dbl> 0, 11, 1, 0, 11, 20, 0, 0, 5, 3, 0, 345, 8, 190, 0, 3, ~
## $ pnew_death     <dbl> 0, 2, 0, NA, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, NA,~
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "03~
## $ consent_cases  <chr> "Agree", "Agree", "Agree", "Not agree", "Not agree", "A~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "Not agree", "Not agree", "Agr~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 11
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-02-20        inp    58908    62620     3712 0.06108880
## 2 2022-02-20 hosp_adult    56750    60478     3728 0.06360255

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     ND        inp   110569   109813      756 0.006860814
## 2     WV   hosp_ped     4497     4703      206 0.044782609
## 3     ME   hosp_ped     1594     1538       56 0.035759898
## 4     MA   hosp_ped    10034     9724      310 0.031379694
## 5     IN   hosp_ped    15429    15261      168 0.010948192
## 6     KY   hosp_ped    15750    15913      163 0.010295929
## 7     VA   hosp_ped    14705    14555      150 0.010252905
## 8     NJ   hosp_ped    16251    16415      164 0.010041021
## 9     NV   hosp_ped     4105     4067       38 0.009300049
## 10    SC   hosp_ped     7732     7661       71 0.009224972
## 11    AL   hosp_ped    17872    17976      104 0.005802276
## 12    VT   hosp_ped      360      362        2 0.005540166
## 13    KS   hosp_ped     4025     4005       20 0.004981320
## 14    NM   hosp_ped     6428     6457       29 0.004501358
## 15    IA   hosp_ped     6509     6481       28 0.004311008
## 16    NH   hosp_ped      761      758        3 0.003949967
## 17    FL   hosp_ped    82260    82509      249 0.003022413
## 18    TN   hosp_ped    18633    18581       52 0.002794647
## 19    WY   hosp_ped      784      786        2 0.002547771
## 20    CO   hosp_ped    18126    18084       42 0.002319801
## 21    SD   hosp_ped     3899     3891        8 0.002053915
## 22    GA   hosp_ped    43658    43742       84 0.001922197
## 23    AR   hosp_ped    10931    10911       20 0.001831334
## 24    UT   hosp_ped     7634     7621       13 0.001704359
## 25    CT   hosp_ped     5640     5649        9 0.001594472
## 26    HI   hosp_ped     2016     2019        3 0.001486989
## 27    MS   hosp_ped     9380     9368       12 0.001280137
## 28    AZ   hosp_ped    23979    23949       30 0.001251878
## 29    IL   hosp_ped    36164    36121       43 0.001189735
## 30    MN   hosp_ped    13210    13224       14 0.001059242
## 31    ND hosp_adult   104829   102042     2787 0.026944328
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 39,269
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 28,760
## Columns: 82
## $ date                                   <date> 2022-03-03, 2022-03-03, 2022-0~
## $ MMWR_week                              <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9~
## $ state                                  <chr> "NE", "NC", "TX", "CA", "AL", "~
## $ Distributed                            <dbl> 3775510, 20928600, 58996495, 86~
## $ Distributed_Janssen                    <dbl> 149600, 917900, 2609300, 368570~
## $ Distributed_Moderna                    <dbl> 1331380, 7886660, 21192040, 307~
## $ Distributed_Pfizer                     <dbl> 2294530, 12124040, 35195155, 51~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 195177, 199546, 203465, 217827,~
## $ Distributed_Per_100k_12Plus            <dbl> 233430, 232915, 244787, 255803,~
## $ Distributed_Per_100k_18Plus            <dbl> 258892, 255621, 273182, 281107,~
## $ Distributed_Per_100k_65Plus            <dbl> 1208330, 1195170, 1579880, 1474~
## $ vxa                                    <dbl> 3086667, 16146189, 44500682, 71~
## $ Administered_12Plus                    <dbl> 2984836, 15667506, 42929166, 69~
## $ Administered_18Plus                    <dbl> 2784330, 14709026, 39448235, 63~
## $ Administered_65Plus                    <dbl> 818837, 4254447, 8966187, 14693~
## $ Administered_Janssen                   <dbl> 93421, 510563, 1535569, 2278802~
## $ Administered_Moderna                   <dbl> 1110596, 6003546, 16331212, 267~
## $ Administered_Pfizer                    <dbl> 1876552, 9631145, 26629492, 426~
## $ Administered_Unk_Manuf                 <dbl> 6098, 935, 4409, 15243, 477, 21~
## $ Admin_Per_100k                         <dbl> 159566, 153948, 153472, 181390,~
## $ Admin_Per_100k_12Plus                  <dbl> 184544, 174364, 178121, 205442,~
## $ Admin_Per_100k_18Plus                  <dbl> 190925, 179655, 182664, 208987,~
## $ Admin_Per_100k_65Plus                  <dbl> 262063, 242959, 240108, 251683,~
## $ Recip_Administered                     <dbl> 3099534, 16045766, 43251399, 71~
## $ Administered_Dose1_Recip               <dbl> 1343086, 8641769, 20646737, 323~
## $ Administered_Dose1_Pop_Pct             <dbl> 69.4, 82.4, 71.2, 81.8, 61.9, 6~
## $ Administered_Dose1_Recip_12Plus        <dbl> 1287672, 8369683, 19753177, 309~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 79.6, 93.1, 82.0, 91.9, 71.1, 7~
## $ Administered_Dose1_Recip_18Plus        <dbl> 1192672, 7856851, 17992902, 284~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.8, 95.0, 83.3, 92.9, 73.8, 7~
## $ Administered_Dose1_Recip_65Plus        <dbl> 306831, 2160730, 3621726, 59741~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 1210303, 6231369, 17444705, 278~
## $ vxcpoppct                              <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 5~
## $ Series_Complete_12Plus                 <dbl> 1164264, 6032933, 16838920, 267~
## $ Series_Complete_12PlusPop_Pct          <dbl> 72.0, 67.1, 69.9, 79.5, 58.0, 6~
## $ vxcgte18                               <dbl> 1078877, 5641201, 15431387, 245~
## $ vxcgte18pct                            <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 6~
## $ vxcgte65                               <dbl> 284820, 1501131, 3209108, 51872~
## $ vxcgte65pct                            <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 8~
## $ Series_Complete_Janssen                <dbl> 87478, 478497, 1339798, 2070288~
## $ Series_Complete_Moderna                <dbl> 412033, 2159277, 6030460, 95834~
## $ Series_Complete_Pfizer                 <dbl> 709218, 3593448, 10073548, 1620~
## $ Series_Complete_Unk_Manuf              <dbl> 1574, 147, 899, 4865, 654, 375,~
## $ Series_Complete_Janssen_12Plus         <dbl> 87453, 478469, 1339351, 2069676~
## $ Series_Complete_Moderna_12Plus         <dbl> 411994, 2159160, 6029642, 95826~
## $ Series_Complete_Pfizer_12Plus          <dbl> 663259, 3395158, 9469060, 15076~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 1558, 146, 867, 4805, 654, 373,~
## $ Series_Complete_Janssen_18Plus         <dbl> 87386, 477036, 1337802, 2062395~
## $ Series_Complete_Moderna_18Plus         <dbl> 411823, 2156126, 6025533, 95567~
## $ Series_Complete_Pfizer_18Plus          <dbl> 578185, 3007897, 8067213, 12955~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 1483, 142, 839, 4500, 650, 366,~
## $ Series_Complete_Janssen_65Plus         <dbl> 6942, 54470, 177453, 201180, 36~
## $ Series_Complete_Moderna_65Plus         <dbl> 138470, 721440, 1524709, 262127~
## $ Series_Complete_Pfizer_65Plus          <dbl> 138496, 725155, 1506622, 236331~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 912, 66, 324, 1458, 415, 213, 1~
## $ Additional_Doses                       <dbl> 585237, 1574890, 6257276, 13507~
## $ Additional_Doses_Vax_Pct               <dbl> 48.4, 25.3, 35.9, 48.5, 34.4, 3~
## $ Additional_Doses_12Plus                <dbl> 585134, 1574750, 6256853, 13506~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 50.3, 26.1, 37.2, 50.5, 34.9, 3~
## $ Additional_Doses_18Plus                <dbl> 565479, 1527656, 6053696, 12967~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 52.4, 27.1, 39.2, 52.8, 36.3, 4~
## $ Additional_Doses_50Plus                <dbl> 364760, 1031758, 3720211, 72429~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 65.5, 34.3, 52.0, 63.8, 47.2, 5~
## $ Additional_Doses_65Plus                <dbl> 211563, 585640, 1945440, 368152~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 74.3, 39.0, 60.6, 71.0, 56.9, 6~
## $ Additional_Doses_Moderna               <dbl> 229241, 693169, 2749043, 586417~
## $ Additional_Doses_Pfizer                <dbl> 349233, 854191, 3412442, 742991~
## $ Additional_Doses_Janssen               <dbl> 6431, 27508, 95589, 213372, 152~
## $ Additional_Doses_Unk_Manuf             <dbl> 332, 22, 202, 522, 81, 490, 73,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 1342804, 8639422, 20641353, 323~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 74.5, 87.5, 76.4, 87.0, 65.9, 7~
## $ Series_Complete_5Plus                  <dbl> 1210243, 6230533, 17443348, 278~
## $ Series_Complete_5PlusPop_Pct           <dbl> 67.1, 63.1, 64.6, 75.0, 53.5, 5~
## $ Administered_5Plus                     <dbl> 3086314, 16143045, 44494008, 71~
## $ Admin_Per_100k_5Plus                   <dbl> 171126, 163419, 164762, 192973,~
## $ Distributed_Per_100k_5Plus             <dbl> 209340, 211864, 218465, 231812,~
## $ Series_Complete_Moderna_5Plus          <dbl> 412011, 2159234, 6029941, 95831~
## $ Series_Complete_Pfizer_5Plus           <dbl> 709196, 3592673, 10072988, 1620~
## $ Series_Complete_Janssen_5Plus          <dbl> 87463, 478480, 1339521, 2069915~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 1573, 146, 898, 4864, 654, 373,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  1.93e+10    3.28e+8   7.85e+7 931901       45489    
## 2 after   1.92e+10    3.27e+8   7.80e+7 927317       39321    
## 3 pctchg  4.93e- 3    4.33e-3   6.98e-3      0.00492     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 39,321
## Columns: 6
## $ date       <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-08-22~
## $ state      <chr> "KS", "UT", "CO", "AR", "AR", "CO", "UT", "MA", "HI", "TX",~
## $ tot_cases  <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 636992, 704796, ~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 3787, 17818, 883, 33124, 7~
## $ new_cases  <dbl> 0, 1060, 677, 0, 547, 6962, 0, 451, 69, 24010, 1028, 18811,~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 5, 0, 345, 8, 190, 3, 15, 7, 8, 0, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult    hosp_ped          n
##   <chr>    <dbl>      <dbl>       <dbl>      <dbl>
## 1 before 4.57e+7    3.93e+7 965351      39269     
## 2 after  4.54e+7    3.91e+7 947960      37644     
## 3 pctchg 4.81e-3    4.60e-3      0.0180     0.0414
## 
## 
## Processed for cdcHosp:
## Rows: 37,644
## Columns: 5
## $ date       <date> 2020-10-18, 2020-10-13, 2020-10-12, 2020-10-08, 2020-10-06~
## $ state      <chr> "VT", "NH", "ID", "MT", "HI", "NH", "NC", "DC", "MA", "MT",~
## $ inp        <dbl> 2, 34, 221, 262, 124, 48, 1283, 156, 354, 207, 116, 102, 39~
## $ hosp_adult <dbl> 2, 34, 219, 259, 124, 48, 1246, 141, 347, 206, 109, 101, 38~
## $ hosp_ped   <dbl> 0, 0, 2, 3, 0, 0, 34, 15, 7, 1, 3, 1, 10, 0, 0, 1, 6, 6, 7,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 2.79e+11 1.18e+11 1050491.    3.15e+10 1622354.    1.11e+11 1255957.   
## 2 after  1.34e+11 5.72e+10  882060.    1.52e+10 1450421     5.37e+10 1065505.   
## 3 pctchg 5.20e- 1 5.16e- 1       0.160 5.16e- 1       0.106 5.17e- 1       0.152
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 22,695
## Columns: 9
## $ date        <date> 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-0~
## $ state       <chr> "NE", "NC", "TX", "CA", "AL", "SC", "WV", "MN", "CO", "KS"~
## $ vxa         <dbl> 3086667, 16146189, 44500682, 71671126, 6108052, 7287794, 2~
## $ vxc         <dbl> 1210303, 6231369, 17444705, 27867605, 2466221, 2880832, 10~
## $ vxcpoppct   <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 56.0, 56.8, 68.3, 69.3, 60.3~
## $ vxcgte65    <dbl> 284820, 1501131, 3209108, 5187220, 689667, 807207, 306687,~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 86.1, 83.6, 95.0, 92.0, 89.5~
## $ vxcgte18    <dbl> 1078877, 5641201, 15431387, 24579521, 2300814, 2646739, 94~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 65.6, 66.0, 78.2, 79.1, 71.1~
## 
## Integrated per capita data file:
## Rows: 39,534
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220304, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220304 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220304.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 409,797
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         7690
## 2 Critical Access Hospitals 109641
## 3 Long Term                  28161
## 4 Short Term                264305
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       27
## 2 GU      164
## 3 MP       82
## 4 PR     4506
## 5 VI      164
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        15604   393445             748 409797
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   373556           32923 409797
## 3 icu_beds_used_7_day_avg                   1649   359635           48513 409797
## 4 inpatient_beds_7_day_avg                  1730   406462            1605 409797
## 5 staffed_icu_adult_patients_confirmed_an~  4241   286438          119118 409797
## 6 total_adult_patients_hospitalized_confi~  2362   285557          121878 409797
## 7 total_beds_7_day_avg                     10392   399022             383 409797
## 8 total_icu_beds_7_day_avg                  2064   387368           20365 409797
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220304, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220304 <- postProcessCDCDaily(cdc_daily_220304, 
                                              dataThruLabel="Feb 2022", 
                                              keyDatesBurden=c("2022-02-28", "2021-08-31", 
                                                               "2021-02-28", "2020-08-31"
                                                               ),
                                              keyDatesVaccine=c("2022-02-28", "2021-10-31", 
                                                                "2021-06-30", "2021-02-28"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220304 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220304, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

Peaks and valleys are converted to functional form:

peakValleyCDCDaily <- function(df, 
                               burdenVars=c("new_deaths", "new_cases", "inp"), 
                               burdenWidth=71, 
                               burdenRollMean=7,
                               minPopVax=8000000, 
                               vaxVars=c("vxa", "vxc"), 
                               minDateVax="2020-12-01", 
                               vaxWidth=71, 
                               vaxRollMean=21
                               ) {
    
    # FUNCTION ARGUMENTS
    # df: data frame (can also pass a list that contains data frame "dfPerCapita")
    # burdenVars: variables to be used for burden peaks and valleys
    # burdenWidth: window size to be used for burden data
    # burdenRollMean: rolling mean to use for smoothing burden data
    # minPopVax: minimum population for state vaccines to be plotted
    # vaxVars: variables to be used for vaccines peaks and valleys
    # minDateVax: earliest day to use for vaccines plotting
    # vaxWidth: window size to be used for vaccines data
    # vaxRollMean: rolling mean to use for smoothing vaccines data

    # Only works for specified burdenVars and vaxVars (fix)
    if(!all.equal(sort(burdenVars), sort(c("new_deaths", "new_cases", "inp")))) stop("\nNot yet enabled - burden\n")
    if(!all.equal(sort(vaxVars), sort(c("vxa", "vxc")))) stop("\nNot yet enabled - vaccines\n")
    
    # Extract data frame from df if needed
    if("list" %in% class(df)) df <- df[["dfPerCapita"]]

    # Burden data
    df %>%
        mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
        makePeakValley(numVar=burdenVars, 
                       windowWidth = burdenWidth, 
                       rollMean=burdenRollMean, 
                       facetVar=c("regn"), 
                       fnNumVar=list("new_deaths"=function(x) x, 
                                     "new_cases"=function(x) x/1000,
                                     "inp"=function(x) x/1000
                                     ), 
                       fnPeak=list("new_deaths"=function(x) x+100, 
                                   "new_cases"=function(x) x+10, 
                                   "inp"=function(x) x+10
                                   ),
                       fnValley=list("new_deaths"=function(x) x-100, 
                                     "new_cases"=function(x) x-5, 
                                     "inp"=function(x) x-5
                                     ),
                       useTitle=c("new_deaths"="US coronavirus deaths", 
                                  "new_cases"="US coronavirus cases", 
                                  "inp"="US coronavirus total hospitalized"
                                  ), 
                       yLab=c("new_deaths"=paste0("Rolling ", burdenRollMean, "-day mean deaths"), 
                              "new_cases"=paste0("Rolling ", burdenRollMean, "-day mean cases (000)"), 
                              "inp"=paste0("Rolling ", burdenRollMean, "-day mean in hospital (000)")
                              )
                       )

    # Vaccinations data for states with at least threshold population
    df %>%
        inner_join(getStateData(), by=c("state")) %>%
        filter(pop >= minPopVax) %>%
        select(c("state", "date", all_of(vaxVars))) %>%
        arrange(date, state) %>%
        group_by(state) %>%
        mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
        ungroup() %>%
        mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
        filter(date >= minDateVax) %>%
        makePeakValley(numVar=vaxVars, 
                       windowWidth = vaxWidth, 
                       rollMean=vaxRollMean, 
                       facetVar=c("state"), 
                       fnNumVar=list("vxa"=function(x) x/1000, 
                                     "vxc"=function(x) x/1000
                                     ), 
                       fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400, 
                                   "vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
                                   ),
                       fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400, 
                                     "vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
                                     ),
                       fnGroupFacet=TRUE,
                       useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", minPopVax, ")"), 
                                  "vxc"=paste0("Became fully vaccinated (states with population >= ", minPopVax, ")")
                                  ), 
                       yLab=c("vxa"=paste0("Rolling ", vaxRollMean, "-day mean administered (000)"),
                              "vxc"=paste0("Rolling ", vaxRollMean,"-day mean completed (000)")
                              )
                       )
    
}

peakValleyCDCDaily(cdc_daily_220304)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 5,496 x 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # ... with 5,486 more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220304 <- skinnyHHS(indivHosp_20220304) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220304, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to February 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
createGeoMap(modStateHosp_20220304 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to February 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220416.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220416.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220416.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220304")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220304")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220304")$dfRaw$vax
                    )

cdc_daily_220416 <- readRunCDCDaily(thruLabel="Apr 14, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-02-27 new_deaths      336      207      129 0.47513812
## 2  2022-02-26 new_deaths      553      416      137 0.28276574
## 3  2022-02-20 new_deaths      563      441      122 0.24302789
## 4  2021-07-30 new_deaths      651      521      130 0.22184300
## 5  2022-02-19 new_deaths      732      612      120 0.17857143
## 6  2022-02-13 new_deaths      693      615       78 0.11926606
## 7  2022-02-21 new_deaths     1089      967      122 0.11867704
## 8  2022-02-12 new_deaths      988      891       97 0.10324641
## 9  2022-02-06 new_deaths      674      609       65 0.10132502
## 10 2022-02-18 new_deaths     2283     2149      134 0.06046931
## 11 2022-02-05 new_deaths     1221     1158       63 0.05296343
## 12 2022-02-26  new_cases    26248    23158     3090 0.12508602
## 13 2021-10-31  new_cases    25456    22766     2690 0.11156733
## 14 2022-02-27  new_cases    18268    16411     1857 0.10709651
## 15 2022-02-28  new_cases    72092    80046     7954 0.10456296
## 16 2021-11-07  new_cases    31372    28368     3004 0.10056913
## 17 2021-11-06  new_cases    35485    32140     3345 0.09892791
## 18 2021-10-30  new_cases    34475    31410     3065 0.09304090
## 19 2021-11-14  new_cases    33631    30649     2982 0.09278158
## 20 2021-10-23  new_cases    36520    33628     2892 0.08245424
## 21 2021-10-24  new_cases    28146    25952     2194 0.08111206
## 22 2021-11-20  new_cases    45749    42759     2990 0.06756451
## 23 2021-11-21  new_cases    38274    35892     2382 0.06423429
## 24 2021-11-13  new_cases    53584    50305     3279 0.06312507
## 25 2021-10-25  new_cases    84093    88971     4878 0.05637221
## 26 2021-11-08  new_cases   116560   122589     6029 0.05042045

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     KY tot_deaths   4418134   4375794    42340 0.009629372
## 2     FL tot_deaths  23037457  22932268   105189 0.004576447
## 3     AL tot_deaths   6469661   6452309    17352 0.002685659
## 4     NC tot_deaths   7407674   7392943    14731 0.001990593
## 5     SC tot_deaths   5380045   5370547     9498 0.001766972
## 6     CO  tot_cases 314009823 311159444  2850379 0.009118743
## 7     DE  tot_cases  61116141  61473234   357093 0.005825839
## 8     KY  tot_cases 286467313 285415770  1051543 0.003677475
## 9     NC  tot_cases 592997222 592074229   922993 0.001557700
## 10    KY new_deaths     14951     13935     1016 0.070345496
## 11    DE new_deaths      2711      2573      138 0.052233157
## 12    AL new_deaths     19167     18407      760 0.040453505
## 13    FL new_deaths     72517     70789     1728 0.024116227
## 14    NC new_deaths     22958     22671      287 0.012579719
## 15    RI new_deaths      3441      3413       28 0.008170411
## 16    CO  new_cases   1345585   1312298    33287 0.025047754
## 17    KY  new_cases   1305049   1282281    22768 0.017599610
## 18    NC  new_cases   2612332   2592991    19341 0.007431239
## 19    DE  new_cases    257219    256051     1168 0.004551211
## 20    SC  new_cases   1463332   1461843     1489 0.001018059
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 48,840
## Columns: 15
## $ date           <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state          <chr> "KS", "AS", "AR", "MP", "AS", "HI", "MA", "PR", "GA", "~
## $ tot_cases      <dbl> 621273, 11, 56199, 37, 0, 661, 704796, 35112, 1187107, ~
## $ conf_cases     <dbl> 470516, NA, NA, 37, NA, NA, 659246, 34791, 937515, 3739~
## $ prob_cases     <dbl> 150757, NA, NA, 0, NA, NA, 45550, 321, 249592, 101649, ~
## $ new_cases      <dbl> 19414, 0, 547, 1, 0, 8, 451, 619, 3829, 1028, 0, 0, 276~
## $ pnew_case      <dbl> 6964, 0, 0, 0, 0, 0, 46, 1, 1144, 264, 0, 0, 317, 0, 0,~
## $ tot_deaths     <dbl> 7162, 0, 674, 2, 0, 17, 17818, 805, 21690, 7488, 0, 140~
## $ conf_death     <dbl> NA, NA, NA, 2, NA, NA, 17458, 624, 18725, 6379, NA, 980~
## $ prob_death     <dbl> NA, NA, NA, 0, NA, NA, 360, 181, 2965, 1109, NA, 4202, ~
## $ new_deaths     <dbl> 21, 0, 11, 0, 0, 0, 5, 3, 7, 8, 0, 0, 3, 0, 69, 34, 0, ~
## $ pnew_death     <dbl> 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ created_at     <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases  <chr> "Agree", NA, "Not agree", "Agree", NA, "Not agree", "Ag~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Not agree", "Agre~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-03-03        inp    39007    41066     2059 0.05142807
## 2 2022-03-03 hosp_adult    37433    39443     2010 0.05229200

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     NH   hosp_ped      876      785       91 0.109572547
## 2     WV   hosp_ped     4828     4653      175 0.036915937
## 3     MA   hosp_ped     9943    10225      282 0.027965093
## 4     AR   hosp_ped    10871    11128      257 0.023364698
## 5     NV   hosp_ped     4183     4271       88 0.020818547
## 6     SC   hosp_ped     7993     7878      115 0.014491840
## 7     KY   hosp_ped    16368    16155      213 0.013098423
## 8     AL   hosp_ped    18331    18119      212 0.011632373
## 9     VA   hosp_ped    14832    14994      162 0.010863005
## 10    DE   hosp_ped     4271     4236       35 0.008228518
## 11    ID   hosp_ped     3406     3433       27 0.007895891
## 12    NJ   hosp_ped    16512    16406      106 0.006440245
## 13    IN   hosp_ped    15652    15740       88 0.005606524
## 14    UT   hosp_ped     7997     8031       34 0.004242575
## 15    MD   hosp_ped    12691    12739       48 0.003775069
## 16    PR   hosp_ped    17374    17309       65 0.003748234
## 17    OK   hosp_ped    21962    22043       81 0.003681400
## 18    TN   hosp_ped    19088    19153       65 0.003399493
## 19    VT   hosp_ped      382      383        1 0.002614379
## 20    FL   hosp_ped    83322    83126      196 0.002355090
## 21    CO   hosp_ped    18479    18439       40 0.002166965
## 22    PA   hosp_ped    45833    45907       74 0.001613255
## 23    HI   hosp_ped     2117     2120        3 0.001416096
## 24    CA   hosp_ped    67230    67137       93 0.001384268
## 25    MO   hosp_ped    33571    33616       45 0.001339545
## 26    WY   hosp_ped      794      795        1 0.001258653
## 27    WA   hosp_ped    11473    11486       13 0.001132454
## 28    LA   hosp_ped    10021    10032       11 0.001097093
## 29    NH hosp_adult    90770    90907      137 0.001508171
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 41,591
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 31,512
## Columns: 82
## $ date                                   <date> 2022-04-15, 2022-04-15, 2022-0~
## $ MMWR_week                              <dbl> 15, 15, 15, 15, 15, 15, 15, 15,~
## $ state                                  <chr> "NV", "SC", "NE", "ND", "CA", "~
## $ Distributed                            <dbl> 5870110, 10352975, 3914910, 136~
## $ Distributed_Janssen                    <dbl> 258700, 451200, 150100, 52800, ~
## $ Distributed_Moderna                    <dbl> 2014400, 4306940, 1372780, 5258~
## $ Distributed_Pfizer                     <dbl> 3597010, 5594835, 2392030, 7883~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 190578, 201079, 202383, 179382,~
## $ Distributed_Per_100k_12Plus            <dbl> 223896, 234232, 242048, 214517,~
## $ Distributed_Per_100k_18Plus            <dbl> 245867, 256418, 268451, 234924,~
## $ Distributed_Per_100k_65Plus            <dbl> 1183560, 1104880, 1252940, 1140~
## $ vxa                                    <dbl> 4846899, 7387622, 3169859, 1070~
## $ Administered_12Plus                    <dbl> 4729850, 7215781, 3059482, 1035~
## $ Administered_18Plus                    <dbl> 4435348, 6828252, 2853809, 9829~
## $ Administered_65Plus                    <dbl> 1192563, 2256911, 847795, 29325~
## $ Administered_Janssen                   <dbl> 187265, 231115, 95046, 39681, 2~
## $ Administered_Moderna                   <dbl> 1659796, 2864880, 1138222, 4068~
## $ Administered_Pfizer                    <dbl> 2999310, 4289420, 1930052, 6234~
## $ Administered_Unk_Manuf                 <dbl> 528, 2207, 6539, 325, 15816, 47~
## $ Admin_Per_100k                         <dbl> 157359, 143485, 163867, 140451,~
## $ Admin_Per_100k_12Plus                  <dbl> 180405, 163254, 189160, 162456,~
## $ Admin_Per_100k_18Plus                  <dbl> 185772, 169119, 195690, 168926,~
## $ Admin_Per_100k_65Plus                  <dbl> 240450, 240860, 271331, 244697,~
## $ Recip_Administered                     <dbl> 4818924, 7391493, 3182205, 1049~
## $ Administered_Dose1_Recip               <dbl> 2308282, 3462294, 1356028, 4934~
## $ Administered_Dose1_Pop_Pct             <dbl> 74.9, 67.2, 70.1, 64.8, 83.2, 7~
## $ Administered_Dose1_Recip_12Plus        <dbl> 2242948, 3363978, 1296952, 4757~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 85.5, 76.1, 80.2, 74.7, 93.2, 8~
## $ Administered_Dose1_Recip_18Plus        <dbl> 2090215, 3165363, 1200872, 4491~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 87.5, 78.4, 82.3, 77.2, 94.2, 8~
## $ Administered_Dose1_Recip_65Plus        <dbl> 502147, 950807, 308194, 120897,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 1864022, 2914901, 1225968, 4170~
## $ vxcpoppct                              <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 6~
## $ Series_Complete_12Plus                 <dbl> 1813258, 2838179, 1175197, 4028~
## $ Series_Complete_12PlusPop_Pct          <dbl> 69.2, 64.2, 72.7, 63.2, 80.5, 7~
## $ vxcgte18                               <dbl> 1694297, 2670106, 1088392, 3803~
## $ vxcgte18pct                            <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 7~
## $ vxcgte65                               <dbl> 415049, 810448, 286617, 102377,~
## $ vxcgte65pct                            <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 9~
## $ Series_Complete_Janssen                <dbl> 172318, 207380, 88579, 36537, 2~
## $ Series_Complete_Moderna                <dbl> 602890, 1039279, 415073, 145324~
## $ Series_Complete_Pfizer                 <dbl> 1088750, 1667852, 720640, 23513~
## $ Series_Complete_Unk_Manuf              <dbl> 64, 390, 1676, 13, 5079, 1680, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 172314, 207331, 88553, 36530, 2~
## $ Series_Complete_Moderna_12Plus         <dbl> 602884, 1039114, 415031, 145318~
## $ Series_Complete_Pfizer_12Plus          <dbl> 1037996, 1591346, 669959, 22103~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 64, 388, 1654, 13, 5014, 1656, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 172270, 206747, 88485, 36382, 2~
## $ Series_Complete_Moderna_18Plus         <dbl> 602778, 1037101, 414851, 145110~
## $ Series_Complete_Pfizer_18Plus          <dbl> 919188, 1425877, 583491, 198891~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 61, 381, 1565, 12, 4699, 1605, ~
## $ Series_Complete_Janssen_65Plus         <dbl> 26151, 31325, 6998, 4376, 20284~
## $ Series_Complete_Moderna_65Plus         <dbl> 190706, 345825, 139225, 48336, ~
## $ Series_Complete_Pfizer_65Plus          <dbl> 198156, 433078, 139467, 49660, ~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 36, 220, 927, 5, 1493, 392, 362~
## $ Additional_Doses                       <dbl> 713544, 1141995, 617137, 168496~
## $ Additional_Doses_Vax_Pct               <dbl> 38.3, 39.2, 50.3, 40.4, 50.2, 5~
## $ Additional_Doses_12Plus                <dbl> 713502, 1141860, 616974, 168479~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 39.3, 40.2, 52.5, 41.8, 52.5, 6~
## $ Additional_Doses_18Plus                <dbl> 694360, 1116070, 594777, 166705~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 41.0, 41.8, 54.6, 43.8, 54.7, 6~
## $ Additional_Doses_50Plus                <dbl> 458721, 822790, 381496, 114424,~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 53.8, 53.4, 68.1, 57.9, 65.5, 7~
## $ Additional_Doses_65Plus                <dbl> 259411, 507047, 220977, 69677, ~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 62.5, 62.6, 77.1, 68.1, 72.7, 8~
## $ Additional_Doses_Moderna               <dbl> 292543, 489262, 242700, 77363, ~
## $ Additional_Doses_Pfizer                <dbl> 408783, 629984, 367091, 88613, ~
## $ Additional_Doses_Janssen               <dbl> 12214, 22236, 6918, 2511, 22302~
## $ Additional_Doses_Unk_Manuf             <dbl> 4, 513, 428, 9, 575, 418, 61, 9~
## $ Administered_Dose1_Recip_5Plus         <dbl> 2308180, 3460120, 1355714, 4931~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 79.7, 71.3, 75.2, 69.7, 88.5, 7~
## $ Series_Complete_5Plus                  <dbl> 1864007, 2913784, 1225903, 4168~
## $ Series_Complete_5PlusPop_Pct           <dbl> 64.4, 60.0, 68.0, 58.9, 76.2, 7~
## $ Administered_5Plus                     <dbl> 4846790, 7384273, 3169464, 1069~
## $ Admin_Per_100k_5Plus                   <dbl> 167444, 152057, 175737, 151118,~
## $ Distributed_Per_100k_5Plus             <dbl> 202797, 213189, 217070, 193090,~
## $ Series_Complete_Moderna_5Plus          <dbl> 602885, 1039202, 415051, 145320~
## $ Series_Complete_Pfizer_5Plus           <dbl> 1088743, 1666860, 720616, 23500~
## $ Series_Complete_Janssen_5Plus          <dbl> 172315, 207334, 88563, 36532, 2~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 64, 388, 1673, 13, 5078, 1675, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  2.28e+10    3.70e+8   7.99e+7 968928       48026    
## 2 after   2.26e+10    3.68e+8   7.93e+7 964235       41514    
## 3 pctchg  5.25e- 3    4.38e-3   7.20e-3      0.00484     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 41,514
## Columns: 6
## $ date       <date> 2022-01-14, 2020-08-22, 2020-06-05, 2021-05-22, 2021-08-01~
## $ state      <chr> "KS", "AR", "HI", "MA", "GA", "OK", "OK", "GA", "GA", "TX",~
## $ tot_cases  <dbl> 621273, 56199, 661, 704796, 1187107, 475578, 1034439, 14780~
## $ tot_deaths <dbl> 7162, 674, 17, 17818, 21690, 7488, 14010, 3176, 1758, 49521~
## $ new_cases  <dbl> 19414, 547, 8, 451, 3829, 1028, 0, 2766, 687, 1199, 0, 29, ~
## $ new_deaths <dbl> 21, 11, 0, 5, 7, 8, 0, 3, 69, 34, 0, 0, 31, 2, 15, 7, 0, 1,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.65e+7    4.01e+7 1012359      41591     
## 2 after  4.63e+7    3.99e+7  994497      39837     
## 3 pctchg 4.78e-3    4.57e-3       0.0176     0.0422
## 
## 
## Processed for cdcHosp:
## Rows: 39,837
## Columns: 5
## $ date       <date> 2020-10-18, 2020-10-17, 2020-10-13, 2020-10-12, 2020-10-08~
## $ state      <chr> "VT", "VT", "NH", "ID", "ND", "ID", "NE", "MS", "DC", "HI",~
## $ inp        <dbl> 2, 3, 34, 221, 218, 191, 316, 516, 156, 123, 198, 116, 102,~
## $ hosp_adult <dbl> 2, 3, 34, 219, 212, 189, 315, 462, 141, 122, 193, 109, 101,~
## $ hosp_ped   <dbl> 0, 0, 0, 2, 6, 2, 6, 4, 15, 1, 5, 3, 1, 1, 0, 0, 1, 6, 32, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 3.28e+11 1.37e+11 1219443.    3.57e+10 1848256.    1.28e+11 1448866.   
## 2 after  1.58e+11 6.64e+10 1022671.    1.73e+10 1645640     6.19e+10 1227708.   
## 3 pctchg 5.19e- 1 5.16e- 1       0.161 5.16e- 1       0.110 5.17e- 1       0.153
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 24,888
## Columns: 9
## $ date        <date> 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-1~
## $ state       <chr> "NV", "SC", "NE", "ND", "CA", "MN", "DE", "WA", "AK", "CT"~
## $ vxa         <dbl> 4846899, 7387622, 3169859, 1070327, 73947936, 10165098, 17~
## $ vxc         <dbl> 1864022, 2914901, 1225968, 417012, 28314115, 3888695, 6696~
## $ vxcpoppct   <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 69.0, 68.8, 72.3, 62.0, 78.8~
## $ vxcgte65    <dbl> 415049, 810448, 286617, 102377, 5246870, 882521, 180253, 1~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 95.0, 95.0, 93.8, 85.9, 95.0~
## $ vxcgte18    <dbl> 1694297, 2670106, 1088392, 380395, 24878761, 3415346, 6047~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 78.8, 78.5, 82.4, 73.0, 87.7~
## 
## Integrated per capita data file:
## Rows: 41,727
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220416, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220416 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220416.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 439,873
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         8246
## 2 Critical Access Hospitals 117679
## 3 Long Term                  30218
## 4 Short Term                283730
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       33
## 2 GU      176
## 3 MP       88
## 4 PR     4824
## 5 VI      176
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        27909   411160             804 439873
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   400911           35644 439873
## 3 icu_beds_used_7_day_avg                   1649   385635           52589 439873
## 4 inpatient_beds_7_day_avg                  1730   436407            1736 439873
## 5 staffed_icu_adult_patients_confirmed_an~  4241   306239          129393 439873
## 6 total_adult_patients_hospitalized_confi~  2362   304424          133087 439873
## 7 total_beds_7_day_avg                     22106   417354             413 439873
## 8 total_icu_beds_7_day_avg                  2064   415848           21961 439873
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220416, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220416 <- postProcessCDCDaily(cdc_daily_220416, 
                                              dataThruLabel="Mar 2022", 
                                              keyDatesBurden=c("2022-03-31", "2021-09-30", 
                                                               "2021-03-31", "2020-09-30"
                                                               ),
                                              keyDatesVaccine=c("2022-03-31", "2021-11-30", 
                                                                "2021-07-31", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220416 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220416, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

Peaks and valleys of key metrics are also plotted:

peakValleyCDCDaily(cdc_daily_220416)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,012 x 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # ... with 6,002 more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220416 <- skinnyHHS(indivHosp_20220416) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220416, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to mid-April 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to mid-April 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220501.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220501.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220501.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220416")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220416")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220416")$dfRaw$vax
                    )

cdc_daily_220501 <- readRunCDCDaily(thruLabel="Apr 30, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-04-14 new_deaths      555      747      192 0.29493088
## 2  2022-04-10 new_deaths       64       49       15 0.26548673
## 3  2022-04-12 new_deaths      430      396       34 0.08232446
## 4  2022-04-13 new_deaths      596      641       45 0.07275667
## 5  2022-04-03 new_deaths      101       94        7 0.07179487
## 6  2022-04-11 new_deaths      324      303       21 0.06698565
## 7  2022-04-09 new_deaths      150      141        9 0.06185567
## 8  2022-04-02 new_deaths      162      154        8 0.05063291
## 9  2022-04-09  new_cases    14426    13247     1179 0.08520941
## 10 2022-04-13  new_cases    47095    51168     4073 0.08289997
## 11 2022-04-10  new_cases    18655    17702      953 0.05242457

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     KY tot_deaths   5084815   5070287    14528 0.002861222
## 2     CO  tot_cases 373235234 372250769   984465 0.002641136
## 3     KY new_deaths     15445     15251      194 0.012640083
## 4     NV new_deaths     10223     10340      117 0.011379663
## 5     AL new_deaths     19552     19502       50 0.002560557
## 6     CO new_deaths     12001     12031       30 0.002496671
## 7     FL new_deaths     73846     73689      157 0.002128309
## 8     SC new_deaths     17733     17698       35 0.001975671
## 9     NC new_deaths     23362     23334       28 0.001199246
## 10    CO  new_cases   1373102   1361600    11502 0.008411885
## 11    NC  new_cases   2643272   2639241     4031 0.001526168
## 12    KY  new_cases   1323254   1321450     1804 0.001364236
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 49,740
## Columns: 15
## $ date           <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state          <chr> "KS", "AS", "AR", "MP", "AS", "CO", "MA", "PR", "GA", "~
## $ tot_cases      <dbl> 621273, 11, 56199, 37, 0, 944337, 704796, 35112, 118710~
## $ conf_cases     <dbl> 470516, NA, NA, 37, NA, 862950, 659246, 34791, 937515, ~
## $ prob_cases     <dbl> 150757, NA, NA, 0, NA, 81387, 45550, 321, 249592, 94752~
## $ new_cases      <dbl> 19414, 0, 547, 1, 0, 10817, 451, 619, 3829, 203, 0, 175~
## $ pnew_case      <dbl> 6964, 0, 0, 0, 0, 931, 46, 1, 1144, 54, 0, 168, 317, 0,~
## $ tot_deaths     <dbl> 7162, 0, 674, 2, 0, 10271, 17818, 805, 21690, 7256, 0, ~
## $ conf_death     <dbl> NA, NA, NA, 2, NA, 9089, 17458, 624, 18725, 6176, 0, 28~
## $ prob_death     <dbl> NA, NA, NA, 0, NA, 1182, 360, 181, 2965, 1080, 0, 5188,~
## $ new_deaths     <dbl> 21, 0, 11, 0, 0, 31, 5, 3, 7, 0, 0, 20, 3, 0, 69, 34, 0~
## $ pnew_death     <dbl> 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, -7, 0, 0, 0, 0, NA, 0,~
## $ created_at     <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases  <chr> "Agree", NA, "Not agree", "Agree", NA, "Agree", "Agree"~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Agree", "Agree", ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta   pctDelta
## 1 2020-07-25 hosp_ped     4621     4270      351 0.07895625

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     AS        inp      354      356        2 0.005633803
## 2     VI        inp     4288     4296        8 0.001863933
## 3     NH   hosp_ped      898      944       46 0.049945711
## 4     ME   hosp_ped     1889     1945       56 0.029212311
## 5     WV   hosp_ped     5138     5034      104 0.020448289
## 6     AR   hosp_ped    11326    11212      114 0.010116248
## 7     ID   hosp_ped     3585     3550       35 0.009810792
## 8     AL   hosp_ped    18802    18972      170 0.009000900
## 9     IN   hosp_ped    16332    16230      102 0.006264971
## 10    NJ   hosp_ped    16929    17023       94 0.005537229
## 11    MO   hosp_ped    35405    35251      154 0.004359149
## 12    PR   hosp_ped    17701    17774       73 0.004115574
## 13    CO   hosp_ped    19247    19322       75 0.003889134
## 14    TN   hosp_ped    20056    20133       77 0.003831894
## 15    NM   hosp_ped     6922     6896       26 0.003763207
## 16    VA   hosp_ped    15517    15465       52 0.003356788
## 17    UT   hosp_ped     8779     8750       29 0.003308803
## 18    AK   hosp_ped     2227     2233        6 0.002690583
## 19    MD   hosp_ped    13808    13771       37 0.002683201
## 20    MS   hosp_ped     9988    10011       23 0.002300115
## 21    KY   hosp_ped    17263    17298       35 0.002025404
## 22    GA   hosp_ped    46480    46386       94 0.002024422
## 23    CA   hosp_ped    70500    70639      139 0.001969689
## 24    FL   hosp_ped    85064    84922      142 0.001670726
## 25    IA   hosp_ped     6846     6835       11 0.001608070
## 26    CT   hosp_ped     6956     6966       10 0.001436575
## 27    SC   hosp_ped     8214     8204       10 0.001218175
## 28    TX   hosp_ped   103805   103689      116 0.001118105
## 29    NV   hosp_ped     4552     4547        5 0.001099022
## 30    AS hosp_adult      350      352        2 0.005698006
## 31    VI hosp_adult     4039     4047        8 0.001978729
## 32    NH hosp_adult    92783    92686       97 0.001045997
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 42,401
## Columns: 135
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> ~
## $ all_pediatric_inpatient_beds                                                 <dbl> ~
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> ~
## $ total_staffed_pediatric_icu_beds                                             <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 32,472
## Columns: 82
## $ date                                   <date> 2022-04-30, 2022-04-30, 2022-0~
## $ MMWR_week                              <dbl> 17, 17, 17, 17, 17, 17, 17, 17,~
## $ state                                  <chr> "IN", "NM", "US", "ME", "KY", "~
## $ Distributed                            <dbl> 13462080, 4474445, 728344715, 3~
## $ Distributed_Janssen                    <dbl> 607200, 187600, 30749100, 15400~
## $ Distributed_Moderna                    <dbl> 4734100, 1748900, 270415980, 13~
## $ Distributed_Pfizer                     <dbl> 8120780, 2537945, 427179635, 19~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 199965, 213391, 219375, 253996,~
## $ Distributed_Per_100k_12Plus            <dbl> 235968, 250328, 256893, 288138,~
## $ Distributed_Per_100k_18Plus            <dbl> 260679, 276031, 282020, 311698,~
## $ Distributed_Per_100k_65Plus            <dbl> 1239900, 1184950, 1329290, 1196~
## $ vxa                                    <dbl> 9524604, 3935871, 575765730, 28~
## $ Administered_12Plus                    <dbl> 9276622, 3800236, 557160223, 27~
## $ Administered_18Plus                    <dbl> 8736719, 3533433, 520923954, 26~
## $ Administered_65Plus                    <dbl> 2742875, 1042375, 146082778, 87~
## $ Administered_Janssen                   <dbl> 305604, 119590, 18703265, 14369~
## $ Administered_Moderna                   <dbl> 3431369, 1598596, 216827616, 11~
## $ Administered_Pfizer                    <dbl> 5756128, 2208144, 339679887, 15~
## $ Administered_Unk_Manuf                 <dbl> 31503, 9541, 554962, 3615, 2531~
## $ Admin_Per_100k                         <dbl> 141478, 187706, 173419, 211476,~
## $ Admin_Per_100k_12Plus                  <dbl> 162604, 212609, 196515, 232502,~
## $ Admin_Per_100k_18Plus                  <dbl> 169177, 217980, 201705, 237494,~
## $ Admin_Per_100k_65Plus                  <dbl> 252627, 276048, 266613, 307600,~
## $ Recip_Administered                     <dbl> 9536900, 4083307, 575765730, 28~
## $ Administered_Dose1_Recip               <dbl> 4135482, 1837743, 257641065, 12~
## $ Administered_Dose1_Pop_Pct             <dbl> 61.4, 87.6, 77.6, 90.4, 66.1, 8~
## $ Administered_Dose1_Recip_12Plus        <dbl> 3990122, 1755545, 247398872, 11~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 69.9, 95.0, 87.3, 95.0, 75.2, 9~
## $ Administered_Dose1_Recip_18Plus        <dbl> 3731041, 1618184, 229910032, 11~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 72.2, 95.0, 89.0, 95.0, 77.6, 9~
## $ Administered_Dose1_Recip_65Plus        <dbl> 1009378, 428113, 56761008, 3298~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 93.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 3690380, 1490068, 219675939, 10~
## $ vxcpoppct                              <dbl> 54.8, 71.1, 66.2, 79.5, 57.4, 6~
## $ Series_Complete_12Plus                 <dbl> 3588448, 1428092, 211424055, 10~
## $ Series_Complete_12PlusPop_Pct          <dbl> 62.9, 79.9, 74.6, 86.7, 65.4, 7~
## $ vxcgte18                               <dbl> 3366149, 1314314, 196498734, 96~
## $ vxcgte18pct                            <dbl> 65.2, 81.1, 76.1, 88.2, 67.5, 7~
## $ vxcgte65                               <dbl> 943206, 356210, 49434951, 28625~
## $ vxcgte65pct                            <dbl> 86.9, 94.3, 90.2, 95.0, 86.3, 9~
## $ Series_Complete_Janssen                <dbl> 281574, 109986, 16953131, 13183~
## $ Series_Complete_Moderna                <dbl> 1254015, 563214, 76433248, 3852~
## $ Series_Complete_Pfizer                 <dbl> 2146277, 814554, 126132484, 551~
## $ Series_Complete_Unk_Manuf              <dbl> 8514, 2314, 157076, 845, 1838, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 281541, 109967, 16948031, 13180~
## $ Series_Complete_Moderna_12Plus         <dbl> 1253947, 563150, 76426770, 3852~
## $ Series_Complete_Pfizer_12Plus          <dbl> 2044486, 752672, 117894600, 510~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 8474, 2303, 154654, 837, 1819, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 281258, 109824, 16921288, 13174~
## $ Series_Complete_Moderna_18Plus         <dbl> 1253587, 562729, 76344491, 3851~
## $ Series_Complete_Pfizer_18Plus          <dbl> 1822963, 639497, 103084542, 448~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 8341, 2264, 148413, 755, 1713, ~
## $ Series_Complete_Janssen_65Plus         <dbl> 31120, 21383, 2360115, 24650, 3~
## $ Series_Complete_Moderna_65Plus         <dbl> 461227, 167308, 23565506, 13124~
## $ Series_Complete_Pfizer_65Plus          <dbl> 446929, 166389, 23443158, 13006~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 3930, 1130, 66172, 294, 846, 36~
## $ Additional_Doses                       <dbl> 1693251, 739576, 100600067, 597~
## $ Additional_Doses_Vax_Pct               <dbl> 45.9, 49.6, 45.8, 55.9, 44.2, 4~
## $ Additional_Doses_12Plus                <dbl> 1689014, 739365, 100571760, 597~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 47.1, 51.8, 47.6, 58.1, 45.5, 4~
## $ Additional_Doses_18Plus                <dbl> 1636009, 706410, 96911167, 5754~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 48.6, 53.7, 49.3, 59.6, 47.2, 4~
## $ Additional_Doses_50Plus                <dbl> 1114515, 442723, 61293070, 3915~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 59.7, 63.5, 60.7, 70.1, 59.1, 6~
## $ Additional_Doses_65Plus                <dbl> 652191, 246351, 33899976, 22276~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 69.1, 69.2, 68.6, 77.8, 68.5, 6~
## $ Additional_Doses_Moderna               <dbl> 659070, 310369, 43251133, 27256~
## $ Additional_Doses_Pfizer                <dbl> 1011148, 418139, 55814930, 3131~
## $ Additional_Doses_Janssen               <dbl> 21456, 10800, 1502478, 10855, 2~
## $ Additional_Doses_Unk_Manuf             <dbl> 1577, 268, 31526, 622, 220, 59,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 4135027, 1837487, 257532058, 12~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 65.5, 93.0, 82.5, 94.9, 70.4, 8~
## $ Series_Complete_5Plus                  <dbl> 3690294, 1490006, 219626527, 10~
## $ Series_Complete_5PlusPop_Pct           <dbl> 58.4, 75.4, 70.3, 83.5, 61.1, 7~
## $ Administered_5Plus                     <dbl> 9524148, 3935594, 575608906, 28~
## $ Admin_Per_100k_5Plus                   <dbl> 150845, 199186, 184333, 221953,~
## $ Distributed_Per_100k_5Plus             <dbl> 213214, 226458, 233246, 266598,~
## $ Series_Complete_Moderna_5Plus          <dbl> 1254006, 563197, 76430070, 3852~
## $ Series_Complete_Pfizer_5Plus           <dbl> 2146218, 814520, 126089538, 551~
## $ Series_Complete_Janssen_5Plus          <dbl> 281557, 109976, 16950044, 13181~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 8513, 2313, 156875, 845, 1837, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  2.40e+10    3.85e+8   8.07e+7 974312       48911    
## 2 after   2.39e+10    3.83e+8   8.00e+7 969594       42279    
## 3 pctchg  5.35e- 3    4.40e-3   7.66e-3      0.00484     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 42,279
## Columns: 6
## $ date       <date> 2022-01-14, 2020-08-22, 2021-12-31, 2021-05-22, 2021-08-01~
## $ state      <chr> "KS", "AR", "CO", "MA", "GA", "OK", "GA", "GA", "TX", "AK",~
## $ tot_cases  <dbl> 621273, 56199, 944337, 704796, 1187107, 449170, 147804, 383~
## $ tot_deaths <dbl> 7162, 674, 10271, 17818, 21690, 7256, 3176, 1758, 49521, 9,~
## $ new_cases  <dbl> 19414, 547, 10817, 451, 3829, 203, 2766, 687, 1199, 7, 1723~
## $ new_deaths <dbl> 21, 11, 31, 5, 7, 0, 3, 69, 34, 0, 127, 31, 0, 9, 15, 7, 0,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.68e+7    4.03e+7 1027403      42401     
## 2 after  4.66e+7    4.01e+7 1008991      40602     
## 3 pctchg 4.82e-3    4.60e-3       0.0179     0.0424
## 
## 
## Processed for cdcHosp:
## Rows: 40,602
## Columns: 5
## $ date       <date> 2020-10-13, 2020-10-12, 2020-10-09, 2020-10-04, 2020-09-22~
## $ state      <chr> "RI", "VT", "RI", "RI", "VT", "RI", "AK", "RI", "DE", "VT",~
## $ inp        <dbl> 124, 0, 116, 92, 1, 85, 50, 75, 89, 6, 12, 160, 69, 33, 70,~
## $ hosp_adult <dbl> 123, 0, 115, 91, 1, 84, 49, 75, 84, 5, 9, 115, 66, 33, 55, ~
## $ hosp_ped   <dbl> 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 3, 46, 0, 0, 15, 147, 0, 1, 0~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 3.45e+11 1.44e+11 1278961.    3.72e+10 1927715.    1.34e+11 1516669.   
## 2 after  1.66e+11 6.96e+10 1072129.    1.80e+10 1714305.    6.48e+10 1284690.   
## 3 pctchg 5.19e- 1 5.16e- 1       0.162 5.16e- 1       0.111 5.16e- 1       0.153
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 25,653
## Columns: 9
## $ date        <date> 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-3~
## $ state       <chr> "IN", "NM", "ME", "KY", "DE", "MA", "CA", "TX", "WA", "SC"~
## $ vxa         <dbl> 9524604, 3935871, 2842688, 6522218, 1813671, 14845867, 750~
## $ vxc         <dbl> 3690380, 1490068, 1069169, 2563070, 672952, 5441750, 28464~
## $ vxcpoppct   <dbl> 54.8, 71.1, 79.5, 57.4, 69.1, 79.0, 72.0, 61.4, 72.6, 56.7~
## $ vxcgte65    <dbl> 943206, 356210, 286250, 647476, 181583, 1124456, 5280949, ~
## $ vxcgte65pct <dbl> 86.9, 94.3, 95.0, 86.3, 95.0, 95.0, 90.5, 87.3, 94.2, 86.6~
## $ vxcgte18    <dbl> 3366149, 1314314, 965876, 2338367, 607641, 4805717, 249983~
## $ vxcgte18pct <dbl> 65.2, 81.1, 88.2, 67.5, 78.9, 86.8, 81.6, 72.5, 82.6, 66.2~
## 
## Integrated per capita data file:
## Rows: 42,492
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220501, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220501 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220501.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 449,805
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         8432
## 2 Critical Access Hospitals 120273
## 3 Long Term                  30899
## 4 Short Term                290201
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       35
## 2 GU      180
## 3 MP       90
## 4 PR     4930
## 5 VI      180
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        32098   416881             826 449805
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   409902           36585 449805
## 3 icu_beds_used_7_day_avg                   1650   394165           53990 449805
## 4 inpatient_beds_7_day_avg                  1728   446299            1778 449805
## 5 staffed_icu_adult_patients_confirmed_an~  4238   313313          132254 449805
## 6 total_adult_patients_hospitalized_confi~  2359   310541          136905 449805
## 7 total_beds_7_day_avg                     26089   423289             427 449805
## 8 total_icu_beds_7_day_avg                  2065   425219           22521 449805
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220501, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501, 
                                              dataThruLabel="Apr 2022", 
                                              keyDatesBurden=c("2022-04-29", "2021-10-31", 
                                                               "2021-04-30", "2020-10-31"
                                                               ),
                                              keyDatesVaccine=c("2022-04-29", "2021-12-31", 
                                                                "2021-08-31", "2021-04-30"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220501, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

The post-process function is working incorrectly on the pediatric data. It appears that a few category labels changed syntax. Function createBurdenPivot() is updated to a better formed grep sequence for the latest data:

burdenPivotList_220501$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n())
## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.
## # A tibble: 19 x 6
## # Groups:   adultPed, confSusp, age [18]
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_co~  4.20e4 42401
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_co~  2.49e5 42401
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_co~  3.65e5 42401
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_co~  4.55e5 42401
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_co~  7.25e5 42401
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_co~  9.29e5 42401
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_co~  9.05e5 42401
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_co~  7.73e5 42401
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_su~  3.35e4 42401
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_su~  2.27e5 42401
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_su~  2.97e5 42401
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_su~  3.03e5 42401
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_su~  4.79e5 42401
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_su~  6.54e5 42401
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_su~  6.32e5 42401
## 16 adult    suspected 80+   previous_day_admission_adult_covid_su~  5.75e5 42401
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covi~  1.30e5 42401
## 18 ped      suspected 0-19  all_pediatric_inpatient_bed_occupied    2.00e7 42401
## 19 ped      suspected 0-19  previous_day_admission_pediatric_covi~  3.19e5 42401
# Create pivoted burden data
createBurdenPivot <- function(lst, 
                              dataThru,
                              minDatePlot="2020-08-01", 
                              plotByState=c(state.abb, "DC")
                              ) {
    
    # FUNCTION ARGUMENTS:
    # lst: a processed list that includes sub-component $dfRaw$cdcHosp
    # dataThru: character string to be used for 'data through'; most commonly MMM-YY
    # minDatePlot: starting date for plots
    # plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
    
    # Convert minDatePlot to Date if passed as character
    if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
    
    # Create the hospitalized by age data
    hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
        select(state, 
               date, 
               grep(x=names(.), pattern="previous_.*ed_\\d.*[9+]$", value=TRUE), 
               grep(x=names(.), pattern="previous_.*pediatric.*ed$", value=TRUE)
        ) %>% 
        pivot_longer(-c(state, date)) %>% 
        mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"), 
               adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"), 
               age=ifelse(adultPed=="ped", 
                          "0-17", 
                          stringr::str_replace_all(string=name, pattern=".*_", replacement="")
               ), 
               age=ifelse(age %in% c("0-17", "18-19"), "0-19", age), 
               div=as.character(state.division)[match(state, state.abb)]
        )
    
    # Create the pivoted burden data
    dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
    
    # Plot for overall trends by age group
    p1 <- hospAge %>% 
        filter(state %in% c(state.abb, "DC"), !is.na(value)) %>% 
        mutate(ageBucket=age) %>% 
        group_by(date, ageBucket) %>% 
        summarize(value=sum(value), .groups="drop") %>% 
        arrange(date) %>%
        group_by(ageBucket) %>% 
        mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>% 
        filter(date >= minDatePlot) %>% 
        ggplot(aes(x=date, y=value7)) + 
        labs(x=NULL, 
             y="Confirmed or suspected COVID admissions (rolling-7 mean)", 
             title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"), 
             subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
        ) + 
        lims(y=c(0, NA))
    
    # Create three main plots of hospitalized by age data
    print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
    print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
    print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
    
    # Plot for trends by state and age group
    if (!isFALSE(plotByState)) {
        p2 <- hospAge %>% 
            filter(state %in% plotByState, !is.na(value)) %>% 
            mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>% 
            group_by(date, state, ageBucket) %>% 
            summarize(value=sum(value), .groups="drop") %>% 
            group_by(ageBucket, state) %>% 
            mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>% 
            filter(date >= minDatePlot) %>% 
            ggplot(aes(x=date, y=value7)) + 
            geom_line(aes(color=ageBucket, group=ageBucket)) + 
            scale_color_discrete("Age\nbucket") + 
            labs(x=NULL, 
                 y="Confirmed or suspected COVID admissions (rolling-7 mean)", 
                 title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
            ) + 
            lims(y=c(0, NA)) + 
            facet_wrap(~state, scales="free_y")
        print(p2)
    }
    
    # Return key data (do not return plot objects)
    list(hospAge=hospAge, dfPivot=dfPivot)
    
}

# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501, 
                                              dataThruLabel="Apr 2022", 
                                              keyDatesBurden=c("2022-04-29", "2021-10-31", 
                                                               "2021-04-30", "2020-10-31"
                                                               ),
                                              keyDatesVaccine=c("2022-04-29", "2021-12-31", 
                                                                "2021-08-31", "2021-04-30"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220501, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220501$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n())
## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.
## # A tibble: 18 x 6
## # Groups:   adultPed, confSusp, age [18]
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con~  41964 42401
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con~ 249178 42401
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con~ 365345 42401
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con~ 454857 42401
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con~ 724912 42401
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con~ 929208 42401
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con~ 905339 42401
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con~ 772981 42401
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus~  33526 42401
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus~ 226590 42401
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus~ 296759 42401
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus~ 303443 42401
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus~ 479067 42401
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus~ 653801 42401
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus~ 632370 42401
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus~ 574885 42401
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid~ 130296 42401
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid~ 318942 42401

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220501)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,192 x 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # ... with 6,182 more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220501 <- skinnyHHS(indivHosp_20220501) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220501, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to April 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
createGeoMap(modStateHosp_20220501 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to April 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

A function is created for hospital post-processing:

hospitalCapacityCDCDaily <- function(df, 
                                     createData=TRUE, 
                                     returnData=createData,
                                     maxCapacity=1.2,
                                     plotSub="start to finish"
                                     ) {
    
    # FUNCTION ARGUMENTS:
    # df: the key data frame
    # createData: boolean, if TRUE then convert df for use in processing
    #                      if FALSE, use df as-is
    # returnData: boolean, should df be returned (defaults to TRUE is modified, FALSE otherwise)
    # maxCapacity: states that exceed this capacity level will not be plotted (explore separately)
    # plotSub: subtitle to use for plots
    
    # Convert data if requested
    if(isTRUE(createData)) df <- skinnyHHS(df) %>% imputeNACapacity() %>% sumImputedHHS()

    # Create ICU summary
    createGeoMap(df, 
                 yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                            "pctICU"=c("label"="Total", "color"="black")
                            ), 
                 fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                               "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                               ), 
                 plotTitle="Average % ICU Capacity Filled by Week", 
                 plotSubtitle=plotSub, 
                 plotScaleLabel="% ICU\nUsed", 
                 returnData=FALSE
                 )

    # Get list of states that may complicate map
    pctState <- df %>% 
        mutate(pctAdult=adult_beds_occupied/adult_beds, pctCovidAdult=adult_beds_covid/adult_beds)
    exclStates <- pctState %>% filter(pctAdult > maxCapacity) %>% count(state) %>% pull(state)
    if(length(exclStates) > 0) plotSub <- paste0(plotSub, "\n(", paste(exclStates, collapse=", "), " data excluded)")

    # Create the adult beds summary    
    createGeoMap(df %>% filter(!(state %in% all_of(exclStates))), 
                 yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                            "pctAdult"=c("label"="Total", "color"="black")
                            ), 
                 fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                               "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                               ), 
                 plotTitle="Average % Adult Beds Capacity Filled by Week", 
                 plotSubtitle=plotSub, 
                 plotScaleLabel="% Adult\nBeds\nUsed", 
                 returnData=FALSE
                 )
    
    # Return the data if requested (defaults to only if createData is TRUE)
    if(isTRUE(returnData)) return(df)
    
}

hospitalCapacityCDCDaily(modStateHosp_20220501, createData=FALSE, plotSub="August 2020 to April 2022")